{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3769158",
   "metadata": {},
   "source": [
    "# 任务一"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ceba9b2",
   "metadata": {},
   "source": [
    "- 试用饼图实现**统计订单时间时长**，数据源'Taxi_sz.csv'\n",
    "- 代码需要注解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9875175d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>22.548067</td>\n",
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       "      <td>22:46:25</td>\n",
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       "        VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0            22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1            22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2            22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3            22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4            22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "...            ...       ...         ...        ...         ...        ...   \n",
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       "\n",
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       "\n",
       "[464717 rows x 7 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据源\n",
    "import pandas as pd\n",
    "data_se = pd.read_csv('./data/Taxi_sz.csv')\n",
    "data_se.head(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f94c3101",
   "metadata": {},
   "outputs": [
    {
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       "      <td>23:49:10</td>\n",
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       "        VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0            22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1            22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2            22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3            22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4            22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "...            ...       ...         ...        ...         ...        ...   \n",
       "464712       36947  22:08:22  113.913734  22.531366  113.997284  22.545650   \n",
       "464713       36947  22:39:12  114.005547  22.548067  113.996330  22.537083   \n",
       "464714       36947  22:49:38  113.994598  22.535049  113.922485  22.496550   \n",
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       "464716       36947  23:37:09  113.927635  22.512568  113.910965  22.487867   \n",
       "\n",
       "           Etime  \n",
       "0       00:10:48  \n",
       "1       00:15:19  \n",
       "2       00:29:06  \n",
       "3       00:54:42  \n",
       "4       01:08:17  \n",
       "...          ...  \n",
       "464712  22:36:53  \n",
       "464713  22:46:25  \n",
       "464714  23:13:15  \n",
       "464715  23:30:32  \n",
       "464716  23:49:10  \n",
       "\n",
       "[464717 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除数据中含有空值的行\n",
    "data_se.dropna(inplace=True)\n",
    "data_se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f184e158",
   "metadata": {},
   "outputs": [
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       "      <th>464714</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:49:38</td>\n",
       "      <td>113.994598</td>\n",
       "      <td>22.535049</td>\n",
       "      <td>113.922485</td>\n",
       "      <td>22.496550</td>\n",
       "      <td>23:13:15</td>\n",
       "      <td>22</td>\n",
       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464715</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:24:24</td>\n",
       "      <td>113.921082</td>\n",
       "      <td>22.513483</td>\n",
       "      <td>113.929817</td>\n",
       "      <td>22.494217</td>\n",
       "      <td>23:30:32</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464716</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:37:09</td>\n",
       "      <td>113.927635</td>\n",
       "      <td>22.512568</td>\n",
       "      <td>113.910965</td>\n",
       "      <td>22.487867</td>\n",
       "      <td>23:49:10</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>464717 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0            22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1            22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2            22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3            22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4            22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "...            ...       ...         ...        ...         ...        ...   \n",
       "464712       36947  22:08:22  113.913734  22.531366  113.997284  22.545650   \n",
       "464713       36947  22:39:12  114.005547  22.548067  113.996330  22.537083   \n",
       "464714       36947  22:49:38  113.994598  22.535049  113.922485  22.496550   \n",
       "464715       36947  23:24:24  113.921082  22.513483  113.929817  22.494217   \n",
       "464716       36947  23:37:09  113.927635  22.512568  113.910965  22.487867   \n",
       "\n",
       "           Etime  SHour  SMinute  SSecond  \n",
       "0       00:10:48      0        3       23  \n",
       "1       00:15:19      0       11       33  \n",
       "2       00:29:06      0       17       13  \n",
       "3       00:54:42      0       36       45  \n",
       "4       01:08:17      1        1       14  \n",
       "...          ...    ...      ...      ...  \n",
       "464712  22:36:53     22        8       22  \n",
       "464713  22:46:25     22       39       12  \n",
       "464714  23:13:15     22       49       38  \n",
       "464715  23:30:32     23       24       24  \n",
       "464716  23:49:10     23       37        9  \n",
       "\n",
       "[464717 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计分析订单的起始时间(时、分、秒)\n",
    "data_se['SHour'] = data_se['Stime'].apply(lambda r: int(r.split(':')[0]))\n",
    "data_se['SMinute'] = data_se['Stime'].apply(lambda r: int(r.split(':')[1]))\n",
    "data_se['SSecond'] = data_se['Stime'].apply(lambda r: int(r.split(':')[2]))\n",
    "data_se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76118be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>Stime</th>\n",
       "      <th>SLng</th>\n",
       "      <th>SLat</th>\n",
       "      <th>ELng</th>\n",
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       "      <td>00:03:23</td>\n",
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       "      <th>1</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:11:33</td>\n",
       "      <td>114.227150</td>\n",
       "      <td>22.554167</td>\n",
       "      <td>114.229218</td>\n",
       "      <td>22.560217</td>\n",
       "      <td>00:15:19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:17:13</td>\n",
       "      <td>114.231354</td>\n",
       "      <td>22.562166</td>\n",
       "      <td>114.255798</td>\n",
       "      <td>22.590967</td>\n",
       "      <td>00:29:06</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>00:36:45</td>\n",
       "      <td>114.240196</td>\n",
       "      <td>22.563650</td>\n",
       "      <td>114.119965</td>\n",
       "      <td>22.566668</td>\n",
       "      <td>00:54:42</td>\n",
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       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22223</td>\n",
       "      <td>01:01:14</td>\n",
       "      <td>114.135414</td>\n",
       "      <td>22.575933</td>\n",
       "      <td>114.166748</td>\n",
       "      <td>22.608267</td>\n",
       "      <td>01:08:17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>464712</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:08:22</td>\n",
       "      <td>113.913734</td>\n",
       "      <td>22.531366</td>\n",
       "      <td>113.997284</td>\n",
       "      <td>22.545650</td>\n",
       "      <td>22:36:53</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>53</td>\n",
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       "    <tr>\n",
       "      <th>464713</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:39:12</td>\n",
       "      <td>114.005547</td>\n",
       "      <td>22.548067</td>\n",
       "      <td>113.996330</td>\n",
       "      <td>22.537083</td>\n",
       "      <td>22:46:25</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>464714</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:49:38</td>\n",
       "      <td>113.994598</td>\n",
       "      <td>22.535049</td>\n",
       "      <td>113.922485</td>\n",
       "      <td>22.496550</td>\n",
       "      <td>23:13:15</td>\n",
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       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464715</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:24:24</td>\n",
       "      <td>113.921082</td>\n",
       "      <td>22.513483</td>\n",
       "      <td>113.929817</td>\n",
       "      <td>22.494217</td>\n",
       "      <td>23:30:32</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>464716</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:37:09</td>\n",
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       "      <td>23:49:10</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>49</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>464717 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0            22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1            22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2            22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3            22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4            22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "...            ...       ...         ...        ...         ...        ...   \n",
       "464712       36947  22:08:22  113.913734  22.531366  113.997284  22.545650   \n",
       "464713       36947  22:39:12  114.005547  22.548067  113.996330  22.537083   \n",
       "464714       36947  22:49:38  113.994598  22.535049  113.922485  22.496550   \n",
       "464715       36947  23:24:24  113.921082  22.513483  113.929817  22.494217   \n",
       "464716       36947  23:37:09  113.927635  22.512568  113.910965  22.487867   \n",
       "\n",
       "           Etime  SHour  SMinute  SSecond  EHour  EMinute  ESecond  \n",
       "0       00:10:48      0        3       23      0       10       48  \n",
       "1       00:15:19      0       11       33      0       15       19  \n",
       "2       00:29:06      0       17       13      0       29        6  \n",
       "3       00:54:42      0       36       45      0       54       42  \n",
       "4       01:08:17      1        1       14      1        8       17  \n",
       "...          ...    ...      ...      ...    ...      ...      ...  \n",
       "464712  22:36:53     22        8       22     22       36       53  \n",
       "464713  22:46:25     22       39       12     22       46       25  \n",
       "464714  23:13:15     22       49       38     23       13       15  \n",
       "464715  23:30:32     23       24       24     23       30       32  \n",
       "464716  23:49:10     23       37        9     23       49       10  \n",
       "\n",
       "[464717 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计分析订单的结束时间(时、分、秒)\n",
    "data_se['EHour'] = data_se['Etime'].apply(lambda r: int(r.split(':')[0]))\n",
    "data_se['EMinute'] = data_se['Etime'].apply(lambda r: int(r.split(':')[1]))\n",
    "data_se['ESecond'] = data_se['Etime'].apply(lambda r: int(r.split(':')[2]))\n",
    "data_se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0abd20c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>22223</td>\n",
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       "      <td>445</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:11:33</td>\n",
       "      <td>114.227150</td>\n",
       "      <td>22.554167</td>\n",
       "      <td>114.229218</td>\n",
       "      <td>22.560217</td>\n",
       "      <td>00:15:19</td>\n",
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       "      <td>33</td>\n",
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       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>226</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:17:13</td>\n",
       "      <td>114.231354</td>\n",
       "      <td>22.562166</td>\n",
       "      <td>114.255798</td>\n",
       "      <td>22.590967</td>\n",
       "      <td>00:29:06</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>713</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>114.240196</td>\n",
       "      <td>22.563650</td>\n",
       "      <td>114.119965</td>\n",
       "      <td>22.566668</td>\n",
       "      <td>00:54:42</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>42</td>\n",
       "      <td>1077</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22223</td>\n",
       "      <td>01:01:14</td>\n",
       "      <td>114.135414</td>\n",
       "      <td>22.575933</td>\n",
       "      <td>114.166748</td>\n",
       "      <td>22.608267</td>\n",
       "      <td>01:08:17</td>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>22:08:22</td>\n",
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       "      <td>22.531366</td>\n",
       "      <td>113.997284</td>\n",
       "      <td>22.545650</td>\n",
       "      <td>22:36:53</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
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       "      <td>1711</td>\n",
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       "    <tr>\n",
       "      <th>464713</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:39:12</td>\n",
       "      <td>114.005547</td>\n",
       "      <td>22.548067</td>\n",
       "      <td>113.996330</td>\n",
       "      <td>22.537083</td>\n",
       "      <td>22:46:25</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>25</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464714</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:49:38</td>\n",
       "      <td>113.994598</td>\n",
       "      <td>22.535049</td>\n",
       "      <td>113.922485</td>\n",
       "      <td>22.496550</td>\n",
       "      <td>23:13:15</td>\n",
       "      <td>22</td>\n",
       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>1417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464715</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:24:24</td>\n",
       "      <td>113.921082</td>\n",
       "      <td>22.513483</td>\n",
       "      <td>113.929817</td>\n",
       "      <td>22.494217</td>\n",
       "      <td>23:30:32</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464716</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:37:09</td>\n",
       "      <td>113.927635</td>\n",
       "      <td>22.512568</td>\n",
       "      <td>113.910965</td>\n",
       "      <td>22.487867</td>\n",
       "      <td>23:49:10</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>49</td>\n",
       "      <td>10</td>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>464717 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0            22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1            22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2            22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3            22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4            22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "...            ...       ...         ...        ...         ...        ...   \n",
       "464712       36947  22:08:22  113.913734  22.531366  113.997284  22.545650   \n",
       "464713       36947  22:39:12  114.005547  22.548067  113.996330  22.537083   \n",
       "464714       36947  22:49:38  113.994598  22.535049  113.922485  22.496550   \n",
       "464715       36947  23:24:24  113.921082  22.513483  113.929817  22.494217   \n",
       "464716       36947  23:37:09  113.927635  22.512568  113.910965  22.487867   \n",
       "\n",
       "           Etime  SHour  SMinute  SSecond  EHour  EMinute  ESecond  Duration  \n",
       "0       00:10:48      0        3       23      0       10       48       445  \n",
       "1       00:15:19      0       11       33      0       15       19       226  \n",
       "2       00:29:06      0       17       13      0       29        6       713  \n",
       "3       00:54:42      0       36       45      0       54       42      1077  \n",
       "4       01:08:17      1        1       14      1        8       17       423  \n",
       "...          ...    ...      ...      ...    ...      ...      ...       ...  \n",
       "464712  22:36:53     22        8       22     22       36       53      1711  \n",
       "464713  22:46:25     22       39       12     22       46       25       433  \n",
       "464714  23:13:15     22       49       38     23       13       15      1417  \n",
       "464715  23:30:32     23       24       24     23       30       32       368  \n",
       "464716  23:49:10     23       37        9     23       49       10       721  \n",
       "\n",
       "[464717 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算时间差(订单的持续时间,单位/秒)\n",
    "data_se['Duration'] = (data_se['EHour']*3600+data_se['EMinute']*60+data_se['ESecond']) - \\\n",
    " (data_se['SHour']*3600+data_se['SMinute']*60+data_se['SSecond'])\n",
    "data_se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7d0ca81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          8\n",
       "1          4\n",
       "2         12\n",
       "3         18\n",
       "4          8\n",
       "          ..\n",
       "464712    29\n",
       "464713     8\n",
       "464714    24\n",
       "464715     7\n",
       "464716    13\n",
       "Name: Duration_minutes, Length: 464717, dtype: int32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# 将上述数据转换为分钟计算,并采用向上取整的方法，只获取整数\n",
    "data_se['Duration_minutes'] = np.ceil(\n",
    " data_se['Duration'] / 60).astype(int) # 将秒转换为分钟\n",
    "data_se['Duration_minutes'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ec6a4ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>Stime</th>\n",
       "      <th>SLng</th>\n",
       "      <th>SLat</th>\n",
       "      <th>ELng</th>\n",
       "      <th>ELat</th>\n",
       "      <th>Etime</th>\n",
       "      <th>SHour</th>\n",
       "      <th>SMinute</th>\n",
       "      <th>SSecond</th>\n",
       "      <th>EHour</th>\n",
       "      <th>EMinute</th>\n",
       "      <th>ESecond</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Duration_minutes</th>\n",
       "      <th>Time_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:03:23</td>\n",
       "      <td>114.167465</td>\n",
       "      <td>22.562468</td>\n",
       "      <td>114.225235</td>\n",
       "      <td>22.552750</td>\n",
       "      <td>00:10:48</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>445</td>\n",
       "      <td>8</td>\n",
       "      <td>0-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:11:33</td>\n",
       "      <td>114.227150</td>\n",
       "      <td>22.554167</td>\n",
       "      <td>114.229218</td>\n",
       "      <td>22.560217</td>\n",
       "      <td>00:15:19</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>226</td>\n",
       "      <td>4</td>\n",
       "      <td>0-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:17:13</td>\n",
       "      <td>114.231354</td>\n",
       "      <td>22.562166</td>\n",
       "      <td>114.255798</td>\n",
       "      <td>22.590967</td>\n",
       "      <td>00:29:06</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>713</td>\n",
       "      <td>12</td>\n",
       "      <td>10-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>114.240196</td>\n",
       "      <td>22.563650</td>\n",
       "      <td>114.119965</td>\n",
       "      <td>22.566668</td>\n",
       "      <td>00:54:42</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>42</td>\n",
       "      <td>1077</td>\n",
       "      <td>18</td>\n",
       "      <td>10-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22223</td>\n",
       "      <td>01:01:14</td>\n",
       "      <td>114.135414</td>\n",
       "      <td>22.575933</td>\n",
       "      <td>114.166748</td>\n",
       "      <td>22.608267</td>\n",
       "      <td>01:08:17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>423</td>\n",
       "      <td>8</td>\n",
       "      <td>0-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464712</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:08:22</td>\n",
       "      <td>113.913734</td>\n",
       "      <td>22.531366</td>\n",
       "      <td>113.997284</td>\n",
       "      <td>22.545650</td>\n",
       "      <td>22:36:53</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>36</td>\n",
       "      <td>53</td>\n",
       "      <td>1711</td>\n",
       "      <td>29</td>\n",
       "      <td>20-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464713</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:39:12</td>\n",
       "      <td>114.005547</td>\n",
       "      <td>22.548067</td>\n",
       "      <td>113.996330</td>\n",
       "      <td>22.537083</td>\n",
       "      <td>22:46:25</td>\n",
       "      <td>22</td>\n",
       "      <td>39</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>25</td>\n",
       "      <td>433</td>\n",
       "      <td>8</td>\n",
       "      <td>0-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464714</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:49:38</td>\n",
       "      <td>113.994598</td>\n",
       "      <td>22.535049</td>\n",
       "      <td>113.922485</td>\n",
       "      <td>22.496550</td>\n",
       "      <td>23:13:15</td>\n",
       "      <td>22</td>\n",
       "      <td>49</td>\n",
       "      <td>38</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>1417</td>\n",
       "      <td>24</td>\n",
       "      <td>20-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464715</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:24:24</td>\n",
       "      <td>113.921082</td>\n",
       "      <td>22.513483</td>\n",
       "      <td>113.929817</td>\n",
       "      <td>22.494217</td>\n",
       "      <td>23:30:32</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>368</td>\n",
       "      <td>7</td>\n",
       "      <td>0-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464716</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:37:09</td>\n",
       "      <td>113.927635</td>\n",
       "      <td>22.512568</td>\n",
       "      <td>113.910965</td>\n",
       "      <td>22.487867</td>\n",
       "      <td>23:49:10</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>49</td>\n",
       "      <td>10</td>\n",
       "      <td>721</td>\n",
       "      <td>13</td>\n",
       "      <td>10-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>464717 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0            22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1            22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2            22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3            22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4            22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "...            ...       ...         ...        ...         ...        ...   \n",
       "464712       36947  22:08:22  113.913734  22.531366  113.997284  22.545650   \n",
       "464713       36947  22:39:12  114.005547  22.548067  113.996330  22.537083   \n",
       "464714       36947  22:49:38  113.994598  22.535049  113.922485  22.496550   \n",
       "464715       36947  23:24:24  113.921082  22.513483  113.929817  22.494217   \n",
       "464716       36947  23:37:09  113.927635  22.512568  113.910965  22.487867   \n",
       "\n",
       "           Etime  SHour  SMinute  SSecond  EHour  EMinute  ESecond  Duration  \\\n",
       "0       00:10:48      0        3       23      0       10       48       445   \n",
       "1       00:15:19      0       11       33      0       15       19       226   \n",
       "2       00:29:06      0       17       13      0       29        6       713   \n",
       "3       00:54:42      0       36       45      0       54       42      1077   \n",
       "4       01:08:17      1        1       14      1        8       17       423   \n",
       "...          ...    ...      ...      ...    ...      ...      ...       ...   \n",
       "464712  22:36:53     22        8       22     22       36       53      1711   \n",
       "464713  22:46:25     22       39       12     22       46       25       433   \n",
       "464714  23:13:15     22       49       38     23       13       15      1417   \n",
       "464715  23:30:32     23       24       24     23       30       32       368   \n",
       "464716  23:49:10     23       37        9     23       49       10       721   \n",
       "\n",
       "        Duration_minutes Time_Category  \n",
       "0                      8          0-10  \n",
       "1                      4          0-10  \n",
       "2                     12         10-20  \n",
       "3                     18         10-20  \n",
       "4                      8          0-10  \n",
       "...                  ...           ...  \n",
       "464712                29         20-30  \n",
       "464713                 8          0-10  \n",
       "464714                24         20-30  \n",
       "464715                 7          0-10  \n",
       "464716                13         10-20  \n",
       "\n",
       "[464717 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义时间范围和对应的标签\n",
    "bins = [0, 10, 20, 30, 60, float('inf')] # 时间范围\n",
    "labels = ['0-10', '10-20', '20-30','30-60', '60+'] # 对应的标签（单位/分钟）\n",
    "# 利用 cut 函数根据时间范围划分乘车时间并添加新列 Time_Category\n",
    "data_se['Time_Category'] = pd.cut(\n",
    " data_se['Duration_minutes'], bins=bins, labels=labels, right=False)\n",
    "data_se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "98abdcb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Time_Category</th>\n",
       "      <th>count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0-10</td>\n",
       "      <td>175322</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10-20</td>\n",
       "      <td>165976</td>\n",
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       "      <td>20-30</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30-60</td>\n",
       "      <td>45186</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60+</td>\n",
       "      <td>9152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Time_Category   count\n",
       "0          0-10  175322\n",
       "1         10-20  165976\n",
       "2         20-30   62502\n",
       "3         30-60   45186\n",
       "4           60+    9152"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每个时间段数量,并以DataFrame格式输出\n",
    "time_counts = data_se['Time_Category'].value_counts().rename(\n",
    "    'count').reset_index()\n",
    "time_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5ee498d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制饼图实现订单时长数量分布图\n",
    "import matplotlib.pyplot as plt\n",
    "#设置中文显示\n",
    "plt.rcParams['font.sans-serif']=['SimSun']\n",
    "#绘制一个6x6的画布限制饼图大小\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.pie(time_counts['count'],labels=time_counts['Time_Category'],\n",
    "       autopct='%1.2f%%')#设置百分占比\n",
    "plt.title(\"订单时长分布饼图\")\n",
    "plt.legend()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4292e4c8",
   "metadata": {},
   "source": [
    "# 任务二"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861bc0a0",
   "metadata": {},
   "source": [
    "统计**不同时段的速度**。数据源--清洗后的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "570e80fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def differentSpeed():\n",
    "\n",
    "    df = pd.read_csv(r'./data/Taxi_sz_data.csv')\n",
    "#     df = df[-(df['Speed'] == 0)]\n",
    "    #删除Speed列含有0的数据行\n",
    "    df['Speed'] = df['Speed'].replace(0,np.nan)\n",
    "    df.dropna()\n",
    "\n",
    "    # 假设 df 包含以时间格式表示的 'Stime' 列\n",
    "    df['Stime'] = pd.to_datetime(df['Stime'], format='%H:%M:%S')\n",
    "\n",
    "    # 为不同时间段创建区间\n",
    "    bins = pd.cut(df['Stime'].dt.hour, bins=[8, 10, 12, 14, 16, 18, 20, 22, 24], labels=[\n",
    "                  '8-10', '10-12', '12-14', '14-16', '16-18', '18-20', '20-22', '22-24'])\n",
    "\n",
    "    # 计算每个时间段的最大、平均和最小速度\n",
    "    grouped_df = df.groupby(bins)['Speed'].agg(\n",
    "        ['max', 'mean', 'min']).reset_index()\n",
    "    # 创建新的 DataFrame 以适应 plot 函数\n",
    "    data = pd.DataFrame({\n",
    "        '时间段': ['8-10', '10-12', '12-14', '14-16', '16-18', '18-20', '20-22', '22-24'],\n",
    "        '最快速度': grouped_df['max'].tolist(),\n",
    "        '平均速度': grouped_df['mean'].tolist(),\n",
    "        '最小速度': grouped_df['min'].tolist()\n",
    "    })\n",
    "    #设置中文显示\n",
    "    plt.rcParams['font.sans-serif']=['simsun']\n",
    "    # 绘图\n",
    "    data.set_index('时间段').plot(kind='bar', rot=0)\n",
    "    plt.xlabel('时间段', fontproperties='simsun')\n",
    "    plt.ylabel('速度', fontproperties='simsun')\n",
    "    #设置y轴刻度\n",
    "    plt.ylim([0,150])\n",
    "    plt.title('不同时间段的速度统计', fontproperties='simsun')\n",
    "#     plt.rcParams['font.family'] = 'simsun'\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "differentSpeed()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089bc68d",
   "metadata": {},
   "source": [
    "# 任务三"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b1d327",
   "metadata": {},
   "source": [
    "不同时间段载有乘客的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "048bbbbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据源---Taxi_sz.csv\n",
    "import pandas as pd\n",
    "from pylab import mpl\n",
    "data_se = pd.read_csv('./data/Taxi_sz.csv')\n",
    "# 提取小时值\n",
    "data_se['Hour'] = data_se['Stime'].str.slice(0, 2)\n",
    "# 转换数据结构\n",
    "hourCount = data_se.groupby(\n",
    "    'Hour')['VehicleNum'].count().rename('count').reset_index()\n",
    "# 分析绘图\n",
    "mpl.rcParams[\"font.sans-serif\"] = ['SimSun']\n",
    "plt.xlabel('时间/h')\n",
    "plt.ylabel('车数/辆')\n",
    "plt.title('各个时间段行驶的有乘客的车辆数目')\n",
    "plt.plot(hourCount['Hour'], hourCount['count'])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c24174e4",
   "metadata": {},
   "source": [
    "# 任务四--任务--统计不同车辆的平均行驶速度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3991e91e-0b7a-4314-9127-196c86602925",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as ply\n",
    "import matplotlib as mpl\n",
    "#读取数据\n",
    "data_clean=pd.read_csv('./data/TaxiData-Clean.csv')\n",
    "#自定义判断车牌号是否正确的函数\n",
    "def isVehicle(VehicleNum):\n",
    "    taxiDataVehicleNum =data_clean['VehicleNum'].unique()\n",
    "    return VehicleNum in taxiDataVehicleNum\n",
    "\n",
    "def meanSpeed(VehicleNum):\n",
    "    i=VehicleNum\n",
    "    num=1\n",
    "    while (isVehicle(i)==False & num<4):\n",
    "        print('车辆牌号不存在，请重新输入（共有 3 次机会）！')\n",
    "        i=eval(input())\n",
    "        num+=1\n",
    "        if num==3:\n",
    "            print(\"机会已用完\")\n",
    "        else:\n",
    "            continue\n",
    "    data_se['Hour'] = data_se['Stime'].str.slice(0, 2)\n",
    "    taxiDataByVehicleNum = data_se[(data_se['VehicleNum'] == i)]\n",
    "    meanSpeed = taxiDataByVehicleNum.groupby(\n",
    "    'Hour')['Speed'].mean().rename('vehicleSpeedMean').reset_index()\n",
    "    # display(meanSpeed)\n",
    "    # 开始画图\n",
    "    #mpl.rcParams[\"font.sans-serif\"] = ['SimHei']\n",
    "    #mpl.rcParams[\"axes.unicode_minus\"] = False\n",
    "    plt.xlabel(\"时间/小时\")\n",
    "    plt.ylabel(\"平均速度\")\n",
    "    plt.title(\"车牌号为:\"+VehicleNum+\"出租车的平均速度\")\n",
    "    plt.plot(meanSpeed['Hour'], meanSpeed['vehicleSpeedMean'])\n",
    "    plt.show()\n",
    "#判断车牌号“22271”是否存在\n",
    "meanSpeed('22271')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8440a40e",
   "metadata": {},
   "source": [
    "# 任务五--展示出租车一天第一次载客时的连续行驶路径¶"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec7b4b54",
   "metadata": {},
   "source": [
    "任务--展示出租车一天第一次载客时的连续行驶路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f312692-972a-4ac9-b08e-03b6f90f3e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出经纬度   ---需要判断车牌号的正确性\n",
    "import pandas as pd\n",
    "\n",
    "#读取出租车数据文件\n",
    "taxi = pd.read_csv('./data/Taxi_sz_data.csv') \n",
    "#获取用户输入的车牌号\n",
    "num = eval(input('请输入需要导出经纬度数据的车牌号：'))\n",
    "# 根据车牌号筛选出相应数据并重置索引\n",
    "taxi_num = taxi[taxi['VehicleNum'] == num].reset_index()\n",
    "# 将时间转换为秒数\n",
    "for i in range(len(taxi_num)):\n",
    "    time = (pd.to_datetime(taxi_num['Stime'][i]\n",
    "                           ) - pd.to_datetime('00:00:00')).seconds\n",
    "    taxi_num['Stime'][i] = time\n",
    "# 按车牌号和时间排序数据\n",
    "taxi_time = taxi_num.sort_values(by=['VehicleNum', 'Stime']).reset_index()\n",
    "# 提取经纬度数据\n",
    "taxi_loc = taxi_time[['Lng', 'Lat']]\n",
    "values = taxi_loc.values\n",
    "# 写入经纬度数据到文件\n",
    "with open(str(num) + \"taxi_line.txt\", mode=\"a\", encoding=\"utf-8\") as f:   # 保存在/data该如何实现？？？\n",
    "    f.seek(0)\n",
    "    f.truncate()\n",
    "    for i in range(len(taxi_loc)):\n",
    "        try:\n",
    "            if taxi_time['OpenStatus'][i] == 1 and taxi_time['OpenStatus'][i+1] == 1:\n",
    "                if values[i][0] == values[i-1][0] and values[i][1] == values[i-1][1]:\n",
    "                    continue\n",
    "                lng = float(values[i][0])\n",
    "                lat = float(values[i][1])\n",
    "                if lng == \"\" or lat == \"\":\n",
    "                    continue\n",
    "                dic = {'lat': lat, 'lng': lng}\n",
    "                f.write(str(dic))\n",
    "                if i != len(taxi_loc) - 1:\n",
    "                    f.write(\",\\n\")\n",
    "            elif taxi_time['OpenStatus'][i] == 1 and taxi_time['OpenStatus'][i+1] == 0:\n",
    "                if values[i][0] == values[i-1][0] and values[i][1] == values[i-1][1]:\n",
    "                    continue\n",
    "                lng = float(values[i][0])\n",
    "                lat = float(values[i][1])\n",
    "                if lng == \"\" or lat == \"\":\n",
    "                    continue\n",
    "                dic = {'lat': lat, 'lng': lng}\n",
    "                f.write(str(dic))\n",
    "                if i != len(taxi_loc) - 1:\n",
    "                    f.write(\",\\n\")\n",
    "                break\n",
    "        except:\n",
    "            continue\n",
    "# 输出成功导出信息\n",
    "print('已成功导出！\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48690ae9-29f6-4945-b65b-e707c3ffbd33",
   "metadata": {},
   "source": [
    "# 任务--绘制乘客上车、下车地点的热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da07a6c8-d162-484b-ba59-e1197ad0aa21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出经纬度--开始数据\n",
    "\n",
    "import pandas as pd\n",
    "# 从CSV文件中读取数据\n",
    "c = pd.read_csv(\"./data/Taxi_sz.csv\", encoding=\"utf-8\", low_memory=False)\n",
    "# 对起始经纬度进行分组计数\n",
    "num = c.groupby([\"SLng\", \"SLat\"])[\"VehicleNum\"].count()\n",
    "index1 = num.index\n",
    "values1 = num.values\n",
    "num1 = len(num)\n",
    "# 遍历起始经纬度数据\n",
    "for it in range(num1):\n",
    "    try:\n",
    "        # 提取经度、纬度和计数值\n",
    "        lng = float(index1[it][0])\n",
    "        lat = float(index1[it][1])\n",
    "        count = int(values1[it])\n",
    "        # 检查经纬度是否为空\n",
    "        if lng == \"\" or lat == \"\":\n",
    "            continue\n",
    "        # 构建包含经纬度和计数的字典\n",
    "        dic = {'lat': lat, 'lng': lng, 'count': count}\n",
    "        # 写入数据到文件\n",
    "        if it == 0:\n",
    "            with open(\"./data/start.txt\", mode=\"w\", encoding=\"utf-8\") as f:  # a可以续写，w则是清空再写\n",
    "                dic = str(dic)\n",
    "                f.write(dic + \",\")\n",
    "                f.write(\"\\n\")   # write()  read()\n",
    "        else: \n",
    "            with open(\"./data/start.txt\", mode=\"a\", encoding=\"utf-8\") as f:\n",
    "                if it == num1 - 1:\n",
    "                    dic = str(dic)\n",
    "                    f.write(dic)\n",
    "                else:\n",
    "                    dic = str(dic)\n",
    "                    f.write(dic + \",\")\n",
    "                    f.write(\"\\n\")\n",
    "    except:\n",
    "        continue\n",
    "print(\"导出成功\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68d60c68-daaa-4faa-81be-3eb49e209c59",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bcdb5f43",
   "metadata": {},
   "source": [
    "# 作业"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7464a87b",
   "metadata": {},
   "source": [
    "汽车贷款违约的数据分析。 数据源--data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36ac04ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>account_number</th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>vehicle_year</th>\n",
       "      <th>vehicle_make</th>\n",
       "      <th>bankruptcy_ind</th>\n",
       "      <th>tot_derog</th>\n",
       "      <th>tot_tr</th>\n",
       "      <th>age_oldest_tr</th>\n",
       "      <th>tot_open_tr</th>\n",
       "      <th>...</th>\n",
       "      <th>purch_price</th>\n",
       "      <th>msrp</th>\n",
       "      <th>down_pyt</th>\n",
       "      <th>loan_term</th>\n",
       "      <th>loan_amt</th>\n",
       "      <th>ltv</th>\n",
       "      <th>tot_income</th>\n",
       "      <th>veh_mileage</th>\n",
       "      <th>used_ind</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2314049</td>\n",
       "      <td>11613</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>N</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>17350.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>99.0</td>\n",
       "      <td>6550.00</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63539</td>\n",
       "      <td>13449</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>DAEWOO</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19588.54</td>\n",
       "      <td>19788.0</td>\n",
       "      <td>683.54</td>\n",
       "      <td>60</td>\n",
       "      <td>19588.54</td>\n",
       "      <td>99.0</td>\n",
       "      <td>4666.67</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7328510</td>\n",
       "      <td>14323</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>PLYMOUTH</td>\n",
       "      <td>N</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>13595.00</td>\n",
       "      <td>11450.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>19600.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8725187</td>\n",
       "      <td>15359</td>\n",
       "      <td>1</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>3099.00</td>\n",
       "      <td>60</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4275127</td>\n",
       "      <td>15812</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26328.04</td>\n",
       "      <td>22024.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>26328.04</td>\n",
       "      <td>122.0</td>\n",
       "      <td>4144.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5840</th>\n",
       "      <td>2291068</td>\n",
       "      <td>10005156</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>PORSCHE</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>31000.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5841</th>\n",
       "      <td>7647192</td>\n",
       "      <td>10005616</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24970.00</td>\n",
       "      <td>22024.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>24970.00</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5842</th>\n",
       "      <td>5993246</td>\n",
       "      <td>10006591</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20949.00</td>\n",
       "      <td>18950.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>20949.00</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1837.50</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5843</th>\n",
       "      <td>4766566</td>\n",
       "      <td>10010208</td>\n",
       "      <td>0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>MERCURY</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22400.00</td>\n",
       "      <td>28700.0</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>48</td>\n",
       "      <td>17100.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5844</th>\n",
       "      <td>1928782</td>\n",
       "      <td>10010219</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>JEEP</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19609.90</td>\n",
       "      <td>20145.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52</td>\n",
       "      <td>19609.90</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5845 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      application_id  account_number  bad_ind  vehicle_year vehicle_make  \\\n",
       "0            2314049           11613        1        1998.0         FORD   \n",
       "1              63539           13449        0        2000.0       DAEWOO   \n",
       "2            7328510           14323        1        1998.0     PLYMOUTH   \n",
       "3            8725187           15359        1        1997.0         FORD   \n",
       "4            4275127           15812        0        2000.0       TOYOTA   \n",
       "...              ...             ...      ...           ...          ...   \n",
       "5840         2291068        10005156        0        1997.0      PORSCHE   \n",
       "5841         7647192        10005616        0        2000.0       TOYOTA   \n",
       "5842         5993246        10006591        0        1997.0    CHEVROLET   \n",
       "5843         4766566        10010208        0        1999.0      MERCURY   \n",
       "5844         1928782        10010219        0        2000.0         JEEP   \n",
       "\n",
       "     bankruptcy_ind  tot_derog  tot_tr  age_oldest_tr  tot_open_tr  ...  \\\n",
       "0                 N        7.0     9.0           64.0          2.0  ...   \n",
       "1                 N        0.0    21.0          240.0         11.0  ...   \n",
       "2                 N        7.0    10.0           60.0          NaN  ...   \n",
       "3                 N        3.0    10.0           35.0          5.0  ...   \n",
       "4                 N        0.0    10.0          104.0          2.0  ...   \n",
       "...             ...        ...     ...            ...          ...  ...   \n",
       "5840              N        0.0    21.0          417.0          4.0  ...   \n",
       "5841              Y        2.0     8.0           62.0          5.0  ...   \n",
       "5842              N        0.0     6.0           30.0          4.0  ...   \n",
       "5843              N        0.0     9.0           67.0          7.0  ...   \n",
       "5844              N        1.0    34.0          130.0          8.0  ...   \n",
       "\n",
       "      purch_price     msrp  down_pyt  loan_term  loan_amt    ltv  tot_income  \\\n",
       "0        17200.00  17350.0      0.00         36  17200.00   99.0     6550.00   \n",
       "1        19588.54  19788.0    683.54         60  19588.54   99.0     4666.67   \n",
       "2        13595.00  11450.0      0.00         60  10500.00   92.0     2000.00   \n",
       "3        12999.00  12100.0   3099.00         60  10800.00  118.0     1500.00   \n",
       "4        26328.04  22024.0      0.00         60  26328.04  122.0     4144.00   \n",
       "...           ...      ...       ...        ...       ...    ...         ...   \n",
       "5840         0.00  31000.0      0.00         36  31000.00  100.0     5000.00   \n",
       "5841     24970.00  22024.0      0.00         60  24970.00  117.0     2400.00   \n",
       "5842     20949.00  18950.0      0.00         36  20949.00  113.0     1837.50   \n",
       "5843     22400.00  28700.0   5300.00         48  17100.00   60.0    28000.00   \n",
       "5844     19609.90  20145.0      0.00         52  19609.90   97.0     2700.00   \n",
       "\n",
       "      veh_mileage  used_ind  weight  \n",
       "0         24000.0         1    1.00  \n",
       "1            22.0         0    4.75  \n",
       "2         19600.0         1    1.00  \n",
       "3         10000.0         1    1.00  \n",
       "4            14.0         0    4.75  \n",
       "...           ...       ...     ...  \n",
       "5840      45000.0         1    4.75  \n",
       "5841         21.0         0    4.75  \n",
       "5842      25000.0         1    4.75  \n",
       "5843          0.0         0    4.75  \n",
       "5844         12.0         0    4.75  \n",
       "\n",
       "[5845 rows x 25 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as  pd \n",
    "data = pd.read_csv('./data/data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f6e9c43",
   "metadata": {},
   "source": [
    "'申请者ID','帐户号','是否违约','汽车购买时间','汽车制造商','曾经破产标识','五年内信用不良事件数量','全部帐户数量', '账号存续月份数','开户帐户数量','信用卡数量','信用卡欠款余额','信用卡授信额度','信用卡额度使用比例', 'FICO打分','汽车购买金额','建议售价','分期付款的首次交款','贷款期限','贷款金额','贷款金额/售价','月均收入','行驶里程','是否二手车','样本权重'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ef1210e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_id</th>\n",
       "      <th>account_number</th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>vehicle_year</th>\n",
       "      <th>vehicle_make</th>\n",
       "      <th>bankruptcy_ind</th>\n",
       "      <th>tot_derog</th>\n",
       "      <th>tot_tr</th>\n",
       "      <th>age_oldest_tr</th>\n",
       "      <th>tot_open_tr</th>\n",
       "      <th>...</th>\n",
       "      <th>purch_price</th>\n",
       "      <th>msrp</th>\n",
       "      <th>down_pyt</th>\n",
       "      <th>loan_term</th>\n",
       "      <th>loan_amt</th>\n",
       "      <th>ltv</th>\n",
       "      <th>tot_income</th>\n",
       "      <th>veh_mileage</th>\n",
       "      <th>used_ind</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2314049</td>\n",
       "      <td>11613</td>\n",
       "      <td>1</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>N</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>17350.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>17200.00</td>\n",
       "      <td>99.0</td>\n",
       "      <td>6550.00</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63539</td>\n",
       "      <td>13449</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>DAEWOO</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19588.54</td>\n",
       "      <td>19788.0</td>\n",
       "      <td>683.54</td>\n",
       "      <td>60</td>\n",
       "      <td>19588.54</td>\n",
       "      <td>99.0</td>\n",
       "      <td>4666.67</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8725187</td>\n",
       "      <td>15359</td>\n",
       "      <td>1</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>FORD</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12999.00</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>3099.00</td>\n",
       "      <td>60</td>\n",
       "      <td>10800.00</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4275127</td>\n",
       "      <td>15812</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26328.04</td>\n",
       "      <td>22024.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>26328.04</td>\n",
       "      <td>122.0</td>\n",
       "      <td>4144.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8712513</td>\n",
       "      <td>16979</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>DODGE</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26272.72</td>\n",
       "      <td>26375.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>26272.72</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5400.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5840</th>\n",
       "      <td>2291068</td>\n",
       "      <td>10005156</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>PORSCHE</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>31000.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5841</th>\n",
       "      <td>7647192</td>\n",
       "      <td>10005616</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>TOYOTA</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24970.00</td>\n",
       "      <td>22024.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>24970.00</td>\n",
       "      <td>117.0</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5842</th>\n",
       "      <td>5993246</td>\n",
       "      <td>10006591</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20949.00</td>\n",
       "      <td>18950.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36</td>\n",
       "      <td>20949.00</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1837.50</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5843</th>\n",
       "      <td>4766566</td>\n",
       "      <td>10010208</td>\n",
       "      <td>0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>MERCURY</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22400.00</td>\n",
       "      <td>28700.0</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>48</td>\n",
       "      <td>17100.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>28000.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5844</th>\n",
       "      <td>1928782</td>\n",
       "      <td>10010219</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>JEEP</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19609.90</td>\n",
       "      <td>20145.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52</td>\n",
       "      <td>19609.90</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2700.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4105 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      application_id  account_number  bad_ind  vehicle_year vehicle_make  \\\n",
       "0            2314049           11613        1        1998.0         FORD   \n",
       "1              63539           13449        0        2000.0       DAEWOO   \n",
       "3            8725187           15359        1        1997.0         FORD   \n",
       "4            4275127           15812        0        2000.0       TOYOTA   \n",
       "5            8712513           16979        0        2000.0        DODGE   \n",
       "...              ...             ...      ...           ...          ...   \n",
       "5840         2291068        10005156        0        1997.0      PORSCHE   \n",
       "5841         7647192        10005616        0        2000.0       TOYOTA   \n",
       "5842         5993246        10006591        0        1997.0    CHEVROLET   \n",
       "5843         4766566        10010208        0        1999.0      MERCURY   \n",
       "5844         1928782        10010219        0        2000.0         JEEP   \n",
       "\n",
       "     bankruptcy_ind  tot_derog  tot_tr  age_oldest_tr  tot_open_tr  ...  \\\n",
       "0                 N        7.0     9.0           64.0          2.0  ...   \n",
       "1                 N        0.0    21.0          240.0         11.0  ...   \n",
       "3                 N        3.0    10.0           35.0          5.0  ...   \n",
       "4                 N        0.0    10.0          104.0          2.0  ...   \n",
       "5                 Y        2.0    15.0          136.0          4.0  ...   \n",
       "...             ...        ...     ...            ...          ...  ...   \n",
       "5840              N        0.0    21.0          417.0          4.0  ...   \n",
       "5841              Y        2.0     8.0           62.0          5.0  ...   \n",
       "5842              N        0.0     6.0           30.0          4.0  ...   \n",
       "5843              N        0.0     9.0           67.0          7.0  ...   \n",
       "5844              N        1.0    34.0          130.0          8.0  ...   \n",
       "\n",
       "      purch_price     msrp  down_pyt  loan_term  loan_amt    ltv  tot_income  \\\n",
       "0        17200.00  17350.0      0.00         36  17200.00   99.0     6550.00   \n",
       "1        19588.54  19788.0    683.54         60  19588.54   99.0     4666.67   \n",
       "3        12999.00  12100.0   3099.00         60  10800.00  118.0     1500.00   \n",
       "4        26328.04  22024.0      0.00         60  26328.04  122.0     4144.00   \n",
       "5        26272.72  26375.0      0.00         36  26272.72  100.0     5400.00   \n",
       "...           ...      ...       ...        ...       ...    ...         ...   \n",
       "5840         0.00  31000.0      0.00         36  31000.00  100.0     5000.00   \n",
       "5841     24970.00  22024.0      0.00         60  24970.00  117.0     2400.00   \n",
       "5842     20949.00  18950.0      0.00         36  20949.00  113.0     1837.50   \n",
       "5843     22400.00  28700.0   5300.00         48  17100.00   60.0    28000.00   \n",
       "5844     19609.90  20145.0      0.00         52  19609.90   97.0     2700.00   \n",
       "\n",
       "      veh_mileage  used_ind  weight  \n",
       "0         24000.0         1    1.00  \n",
       "1            22.0         0    4.75  \n",
       "3         10000.0         1    1.00  \n",
       "4            14.0         0    4.75  \n",
       "5             1.0         0    4.75  \n",
       "...           ...       ...     ...  \n",
       "5840      45000.0         1    4.75  \n",
       "5841         21.0         0    4.75  \n",
       "5842      25000.0         1    4.75  \n",
       "5843          0.0         0    4.75  \n",
       "5844         12.0         0    4.75  \n",
       "\n",
       "[4105 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#处理数据中的NaN值\n",
    "data.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d59b99e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>bankruptcy_ind</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>4163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>1017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bad_ind bankruptcy_ind  count\n",
       "0        0              N   4163\n",
       "1        1              N   1017\n",
       "2        0              Y    345\n",
       "3        1              Y    103"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bad_bankruptcy = data[['bad_ind','bankruptcy_ind']].value_counts().reset_index()\n",
    "data_bad_bankruptcy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6979dcc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>bankruptcy_ind</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bad_ind</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4163</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1017</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bankruptcy_ind     N    Y\n",
       "bad_ind                  \n",
       "0               4163  345\n",
       "1               1017  103"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_count=data_bad_bankruptcy.pivot(index='bad_ind',columns='bankruptcy_ind',values='count')\n",
    "data_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2731dc9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4163, 345, 1017, 103]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计订单违约和未违约,破产和未破产的数量（）\n",
    "data_bad_bankruptcy = data[['bad_ind','bankruptcy_ind']].value_counts().reset_index()\n",
    "data_bad_bankruptcy\n",
    "#违约和未违约的数量列表\n",
    "bad_ind_count = []\n",
    "#破产和未破产的数量列表\n",
    "bankruptcy_count = []\n",
    "#往列表中追加数据\n",
    "bad_ind_count.append(data_count['N'][0]+data_count['Y'][0])\n",
    "bad_ind_count.append(data_count['N'][1]+data_count['Y'][1])\n",
    "bankruptcy_count.append(data_count['N'][0])\n",
    "bankruptcy_count.append(data_count['Y'][0])\n",
    "bankruptcy_count.append(data_count['N'][1])\n",
    "bankruptcy_count.append(data_count['Y'][1])\n",
    "bankruptcy_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e1639eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制饼图显示违约和未违约的比例\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#绘制4x4的画布\n",
    "size=0.35\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(bad_ind_count,labels=['未违约','违约'],\n",
    "        autopct='%1.2f%%',textprops={'fontsize': 18, 'color': 'r'},\n",
    "        radius=1,wedgeprops=dict(width=size, edgecolor='w'),\n",
    "       )\n",
    "\n",
    "plt.pie(bankruptcy_count,labels=['未违约但破产','未违约未破产','违约破产','违约未破产'],\n",
    "        autopct='%1.2f%%',textprops={'fontsize': 12, 'color': 'black'},\n",
    "        radius=1-size,wedgeprops=dict(width=size, edgecolor='w'))\n",
    "plt.title('用户违约和未违约的比例分布图')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0264e34c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
